Continuous, Accurate, and detailed information on main grain land (MGL) areas is crucial for provisioning food security and making policies affecting sustainable agricultural production. It still lacks a long-term MGL distribution dataset with fine spatial resolution. This study aimed to produce a long-term, high-resolution MGL distribution map for China. Here, we developed the change map of MGL with resolution 30 m in China for the period 1985–2020 using the Landsat image-based random forest algorithm on the GEE platform. Finally, the planting intensity, gain time and loss time of MGL was calculated. Results indicate that our mapping results are highly consistent with the annual planting area of various grain crops according to national statistics. A validation based on 3113 field survey samples with a 30-m resolution showed that the overall accuracy of MGL were 93.57%. The full MGL dataset is freely available at https://doi.org/10.6084/m9.figshare.26212643.v2.

Detecting tree cover is crucial for sustainable land management and climate mitigation. Here we develop an automatic detection algorithm using high-resolution satellite data (<5 m) to map pan-tropical tree cover (2015–2022), enabling identification and change analysis for previously undetected tree cover (PUTC). Our findings reveal that neglecting PUTC represents 17.31 ± 1.78% of the total pan-tropical tree cover. Tree cover net decreased by 61.05 ± 2.36 Mha in both forested areas (63.93%) and non-forested areas (36.07%) between 2015 and 2022. Intense changes in tree cover are primarily observed in regions with PUTC, where the World Cover dataset with a resolution of 10 m often fails to accurately detect tree cover. We also conduct a sensitivity analysis to quantify the contributions f climate factors and anthropogenic impacts (including human activities and land use cover change) to tree cover dynamics. Our findings indicate that 43.98% of tree cover gain is linked to increased precipitation, while 56.03% of tree cover loss is associated with anthropogenic impacts. These findings highlight the need to include undetected tree cover in strategies combating degradation, climate change, and promoting sustainability. Fine-scale mapping can improve biogeochemical cycles modeling and vegetation-climate interactions, improving global change understanding.

Overgrazing plays an important role in the grassland desertification in global drylands. The effectiveness of policies related to grazing directly affects efforts to combat desertification and sustainable rangeland management. However, there remain questions around how the interplay of grassland desertification and poverty affects the implementation of policies. To reveal the effectiveness of the desertification prevention policy that delineates national key ecological function areas (NKEFAs), the main objective was to perform a sustainability assessment and on-site investigation in Northwest China. A parallel index system, which integrates the indices for economic input–output and material supply–demand to represent sustainability, and the indices for interview records from managers and questionnaires from residents to represent the effectiveness of NKEFA policy, was proposed to comprehensively judge the performance of NKEFA policy, and the underlying causes behind undesirable effects were further analyzed. The results indicate that (1) the performance of desertification control policy is related to socioeconomic conditions—a few counties with increased socioeconomic and land resource sustainability (SLS) are peri-urban or resource-rich; (2) the fact that the socioeconomic benefits of the NKEFA policy are not obvious to impoverished farmers greatly reduces their enthusiasm for preventing desertification; and (3) the livelihood needs and defective ecological compensation force residents with underdeveloped comprehensive quality to overdevelop or use grassland resources even though they have received subsidies for conserving grassland. It is concluded that poverty and grassland desertification interact to influence potential policy performance. Our analysis can help decision makers to formulate desertification control policies with multiple goals to achieve sustainable performance in an economy–ecology system.

Forests play a pivotal role in global carbon cycling and biodiversity conservation, yet they face increasing disturbances from both anthropogenic and natural drivers. This study presents the first high-resolution (30 m) global forest disturbance dataset (GFD) for 2000–2020, classifying 11 disturbance types by integrating Landsat-based Continuous Change Detection and Classification (CCDC) time-series analysis with spatial metrics and machine learning. A total of 57 000 expert-validated sample points were used to train and validate a decision tree model, achieving an overall accuracy of 99 %. The results reveal that forestry replanting (44 %), shifting cultivation (24 %), and forest fires (11 %) dominate global forest loss. There are regional differences in global forest disturbance, such as farmland expansion in South America and Africa, forest fires in northern regions, and shifting cultivation in tropical regions. Disturbed forests span 1247.06 Mha, accounting for 31 % of the global forest area. Notably, 3 % of global forests were newly established, primarily in China, India, and Brazil. The spatial consistency analysis (R²= 0.93) highlights a strong overall agreement between the GFD product and other datasets, while the GFD product offers superior spatial resolution. The GFD dataset advances our understanding of forest dynamics and underscores the need for targeted conservation strategies in an era of escalating environmental change. The 30 m resolution GFD generated by this study is openly available at https://doi.org/10.6084/m9.figshare.28465178 (Liu et al., 2025a).